class_docstr: Defines an iterator for language modeling tasks that use BPTT.
  
  Provides contiguous streams of examples together with targets that are
  one timestep further forward, for language modeling training with
  backpropagation through time (BPTT). Expects a Dataset with a single
  example and a single field called 'text' and produces Batches with text and
  target attributes.
  
  Attributes:
      dataset: The Dataset object to load Examples from.
      batch_size: Batch size.
      bptt_len: Length of sequences for backpropagation through time.
      sort_key: A key to use for sorting examples in order to batch together
          examples with similar lengths and minimize padding. The sort_key
          provided to the Iterator constructor overrides the sort_key
          attribute of the Dataset, or defers to it if None.
      train: Whether the iterator represents a train set.
      repeat: Whether to repeat the iterator for multiple epochs. Default: False.
      shuffle: Whether to shuffle examples between epochs.
      sort: Whether to sort examples according to self.sort_key.
          Note that shuffle and sort default to train and (not train).
      device (str or torch.device): A string or instance of `torch.device`
          specifying which device the Variables are going to be created on.
          If left as default, the tensors will be created on cpu. Default: None.
code: def __iter__(self):
      text = self.dataset[0].text
      TEXT = self.dataset.fields["text"]
      TEXT.eos_token = None
      text = text + (
          [TEXT.pad_token]
          * int(
              math.ceil(len(text) / self.batch_size) * self.batch_size
              - len(text)
          )
      )
      data = TEXT.numericalize([text], device=self.device)
      data = data.view(self.batch_size, -1).t().contiguous()
      dataset = Dataset(
          examples=self.dataset.examples,
          fields=[("text", TEXT), ("target", TEXT)],
      )
      while True:
          for i in range(0, len(self) * self.bptt_len, self.bptt_len):
              self.iterations += 1
              seq_len = min(self.bptt_len, len(data) - i - 1)
              batch_text = data[i : i + seq_len]
              batch_target = data[i + 1 : i + 1 + seq_len]
              if TEXT.batch_first:
                  batch_text = batch_text.t().contiguous()
                  batch_target = batch_target.t().contiguous()
              yield Batch.fromvars(
                  dataset,
                  self.batch_size,
                  text=batch_text,
                  target=batch_target,
              )
          if not self.repeat:
              return
  
imports: import logging
  import math
  import random
  import torch
  from torchtext.data.utils import RandomShuffler
  from .batch import Batch
  from .dataset import Dataset
label_desc: Use image node_id 3 for calling the BPTTIterator obj's underlying member method code with example usage: obj.__iter__() without return types